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Measuring online social bubbles

Measuring online social bubbles | Papers | Scoop.it

Social media have become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view. Here we quantitatively measure this kind of social bias at the collective level by mining a massive datasets of web clicks. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to a search baseline. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at both the collective and individual levels in two datasets where individual users can be analyzed—Twitter posts and search logs. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside “social bubbles.” Our results could lead to a deeper understanding of how technology biases our exposure to new information.


Measuring online social bubbles
Dimitar Nikolov, Diego F.M. Oliveira, Alessandro Flammini, Filippo Menczer

http://dx.doi.org/10.7717/peerj-cs.38 ;

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Whom should we sense in “social sensing” - analyzing which users work best for social media now-casting

Given the ever increasing amount of publicly available social media data, there is growing interest in using online data to study and quantify phenomena in the offline “real” world. As social media data can be obtained in near real-time and at low cost, it is often used for “now-casting” indices such as levels of flu activity or unemployment. The term “social sensing” is often used in this context to describe the idea that users act as “sensors”, publicly reporting their health status or job losses. Sensor activity during a time period is then typically aggregated in a “one tweet, one vote” fashion by simply counting. At the same time, researchers readily admit that social media users are not a perfect representation of the actual population. Additionally, users differ in the amount of details of their personal lives that they reveal. Intuitively, it should be possible to improve now-casting by assigning different weights to different user groups.
In this paper, we ask “How does social sensing actually work?” or, more precisely, “Whom should we sense-and whom not-for optimal results?”. We investigate how different sampling strategies affect the performance of now-casting of two common offline indices: flu activity and unemployment rate. We show that now-casting can be improved by (1) applying user filtering techniques and (2) selecting users with complete profiles. We also find that, using the right type of user groups, now-casting performance does not degrade, even when drastically reducing the size of the dataset. More fundamentally, we describe which type of users contribute most to the accuracy by asking if “babblers are better”. We conclude the paper by providing guidance on how to select better user groups for more accurate now-casting.


Whom should we sense in “social sensing” - analyzing which users work best for social media now-casting
Jisun An and Ingmar Weber

EPJ Data Science 2015, 4:22  http://dx.doi.org/10.1140/epjds/s13688-015-0058-9 

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Eigencentrality based on dissimilarity measures reveals central nodes in complex networks

One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in complex networks is proposed. The centrality method used is the eigencentrality which is based on the heuristic that the centrality of a node depends on how central are the nodes in the immediate neighbourhood (like rich get richer phenomenon). This can be described by an eigenvalues problem, however the information of the neighbourhood and the connections between neighbours is not taken in account, neglecting their relevance when is one evaluates the centrality/importance/influence of a node. The contribution calculated by the dissimilarity measure is parameter independent, making the proposed method is also parameter independent. Finally, we perform a comparative study of our method versus other methods reported in the literature, obtaining more accurate and less expensive computational results in most cases.


Eigencentrality based on dissimilarity measures reveals central nodes in complex networks
A. J. Alvarez-Socorro, G. C. Herrera-Almarza & L. A. González-Díaz

Scientific Reports 5, Article number: 17095 (2015)
http://dx.doi.org/10.1038/srep17095 ;

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The Sensorimotor Loop as a Dynamical System: How Regular Motion Primitives May Emerge from Self-Organized Limit Cycles

We investigate the sensorimotor loop of simple robots simulated within the LPZRobots environment from the point of view of dynamical systems theory. For a robot with a cylindrical shaped body and an actuator controlled by a single proprioceptual neuron, we find various types of periodic motions in terms of stable limit cycles. These are self-organized in the sense that the dynamics of the actuator kicks in only, for a certain range of parameters, when the barrel is already rolling, stopping otherwise. The stability of the resulting rolling motions terminates generally, as a function of the control parameters, at points where fold bifurcations of limit cycles occur. We find that several branches of motion types exist for the same parameters, in terms of the relative frequencies of the barrel and of the actuator, having each their respective basins of attractions in terms of initial conditions. For low drivings stable limit cycles describing periodic and drifting back-and-forth motions are found additionally. These modes allow to generate symmetry breaking explorative behavior purely by the timing of an otherwise neutral signal with respect to the cyclic back-and-forth motion of the robot.


The Sensorimotor Loop as a Dynamical System: How Regular Motion Primitives May Emerge from Self-Organized Limit Cycles
Bulcsú Sándor, Tim Jahn, Laura Martin and Claudius Gros

Front. Robot. AI, 02 December 2015 | http://dx.doi.org/10.3389/frobt.2015.00031

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Inside the Fascinating Genome of the World’s Toughest Animal

Inside the Fascinating Genome of the World’s Toughest Animal | Papers | Scoop.it

In the tun state, tardigrades don't need food or water. They can shrug off temperatures close to absolute zero and as high as 151 degrees Celsius. They can withstand the intense pressures of the deep ocean, doses of radiation that would kill other animals, and baths of toxic solvents. And they are, to date, the only animals that have been exposed to the naked vacuum of space and lived to tell the tale—or, at least, lay viable eggs. A new study suggests that this ability might have contributed to their superlative endurance in a strange and roundabout way. It makes them uniquely suited to absorbing foreign genes from bacteria and other organisms—genes that now pepper their genomes to a degree unheard of for animals.

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Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks

Complex systems may have billion components making consensus formation slow and difficult. Recently several overlapping stories emerged from various disciplines, including protein structures, neuroscience and social networks, showing that fast responses to known stimuli involve a network core of few, strongly connected nodes. In unexpected situations the core may fail to provide a coherent response, thus the stimulus propagates to the periphery of the network. Here the final response is determined by a large number of weakly connected nodes mobilizing the collective memory and opinion, i.e. the slow democracy exercising the 'wisdom of crowds'. This mechanism resembles to Kahneman's "Thinking, Fast and Slow" discriminating fast, pattern-based and slow, contemplative decision making. The generality of the response also shows that democracy is neither only a moral stance nor only a decision making technique, but a very efficient general learning strategy developed by complex systems during evolution. The duality of fast core and slow majority may increase our understanding of metabolic, signaling, ecosystem, swarming or market processes, as well as may help to construct novel methods to explore unusual network responses, deep-learning neural network structures and core-periphery targeting drug design strategies.


Fast and slow thinking -- of networks: The complementary 'elite' and 'wisdom of crowds' of amino acid, neuronal and social networks
Peter Csermely

http://arxiv.org/abs/1511.01238 

Complexity Digest's insight:

See Also: http://networkdecisions.linkgroup.hu 

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António F Fonseca's curator insight, November 23, 2015 3:30 AM

Interesting  paper about fast cores and slow periphery,  conflict in the elite vs democratic consensus.

Marcelo Errera's curator insight, November 24, 2015 11:32 AM

Yes, there must be few fasts and many slows.  It's been predicted by CL in many instances.

 

http://www.researchgate.net/publication/273527384_Constructal_Law_Optimization_as_Design_Evolution

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Unbiased metrics of friends’ influence in multi-level networks

The spreading of information is of crucial importance for the modern information society. While we still receive information from mass media and other non-personalized sources, online social networks and influence of friends have become important personalized sources of information. This calls for metrics to measure the influence of users on the behavior of their friends. We demonstrate that the currently existing metrics of friends’ influence are biased by the presence of highly popular items in the data, and as a result can lead to an illusion of friends influence where there is none. We correct for this bias and develop three metrics that allow to distinguish the influence of friends from the effects of item popularity, and apply the metrics on real datasets. We use a simple network model based on the influence of friends and preferential attachment to illustrate the performance of our metrics at different levels of friends’ influence.


Unbiased metrics of friends’ influence in multi-level networks
Alexandre Vidmer, Matúš Medo and Yi-Cheng Zhang

EPJ Data Science 2015, 4:20  http://dx.doi.org/10.1140/epjds/s13688-015-0057-x ;

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Prediction in complex systems: the case of the international trade network

Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can be represented as a complex network where links connect countries with the products that they export. Link prediction techniques based on heat and mass diffusion processes are employed to obtain predictions for products exported in the future. These baseline predictions are improved using a recent metric of country fitness and product similarity. The overall best results are achieved with a newly developed metric of product similarity which takes advantage of causality in the network evolution.


Prediction in complex systems: the case of the international trade network
Alexandre Vidmer, An Zeng, Matúš Medo, Yi-Cheng Zhang

http://arxiv.org/abs/1511.05404 

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Understanding Human-Machine Networks: A Cross-Disciplinary Survey

In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both or personal and professional use. They can have a significant impact by producing synergy and innovations.
The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.


Understanding Human-Machine Networks: A Cross-Disciplinary Survey
Milena Tsvetkova, Taha Yasseri, Eric T. Meyer, J. Brian Pickering, Vegard Engen, Paul Walland, Marika Lüders, Asbjørn Følstad, George Bravos

http://arxiv.org/abs/1511.05324

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Online social networks and offline protest

Large-scale protests occur frequently and sometimes overthrow entire political systems. Meanwhile, online social networks have become an increasingly common component of people’s lives. We present a large-scale longitudinal study that connects online social media behaviors to offline protest. Using almost 14 million geolocated tweets and data on protests from 16 countries during the Arab Spring, we show that increased coordination of messages on Twitter using specific hashtags is associated with increased protests the following day. The results also show that traditional actors like the media and elites are not driving the results. These results indicate social media activity correlates with subsequent large-scale decentralized coordination of protests, with important implications for the future balance of power between citizens and their states.


Online social networks and offline protest
Zachary C Steinert-Threlkeld, Delia Mocanu, Alessandro Vespignani and James Fowler

EPJ Data Science 2015, 4:19  http://dx.doi.org/10.1140/epjds/s13688-015-0056-y ;

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Coupled catastrophes: sudden shifts cascade and hop among interdependent systems

Coupled catastrophes: sudden shifts cascade and hop among interdependent systems | Papers | Scoop.it

An important challenge in several disciplines is to understand how sudden changes can propagate among coupled systems. Examples include the synchronization of business cycles, population collapse in patchy ecosystems, markets shifting to a new technology platform, collapses in prices and in confidence in financial markets, and protests erupting in multiple countries. A number of mathematical models of these phenomena have multiple equilibria separated by saddle-node bifurcations. We study this behaviour in its normal form as fast–slow ordinary differential equations. In our model, a system consists of multiple subsystems, such as countries in the global economy or patches of an ecosystem. Each subsystem is described by a scalar quantity, such as economic output or population, that undergoes sudden changes via saddle-node bifurcations. The subsystems are coupled via their scalar quantity (e.g. trade couples economic output; diffusion couples populations); that coupling moves the locations of their bifurcations. The model demonstrates two ways in which sudden changes can propagate: they can cascade (one causing the next), or they can hop over subsystems. The latter is absent from classic models of cascades. For an application, we study the Arab Spring protests. After connecting the model to sociological theories that have bistability, we use socioeconomic data to estimate relative proximities to tipping points and Facebook data to estimate couplings among countries. We find that although protests tend to spread locally, they also seem to ‘hop' over countries, like in the stylized model; this result highlights a new class of temporal motifs in longitudinal network datasets.

 

 

Coupled catastrophes: sudden shifts cascade and hop among interdependent systems

Charles D. Brummitt, George Barnett, Raissa M. D'Souza

J. R. Soc. Interface 2015 12 20150712; http://dx.doi.org/10.1098/rsif.2015.0712. Published 11 November 2015. Open Access.
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Mechanisms of mutational robustness in transcriptional regulation

Robustness is the invariance of a phenotype in the face of environmental or genetic change. The phenotypes produced by transcriptional regulatory circuits are gene expression patterns that are to some extent robust to mutations. Here we review several causes of this robustness. They include robustness of individual transcription factor binding sites, homotypic clusters of such sites, redundant enhancers, transcription factors, redundant transcription factors, and the wiring of transcriptional regulatory circuits. Such robustness can either be an adaptation by itself, a byproduct of other adaptations, or the result of biophysical principles and non-adaptive forces of genome evolution. The potential consequences of such robustness include complex regulatory network topologies that arise through neutral evolution, as well as cryptic variation, i.e., genotypic divergence without phenotypic divergence. On the longest evolutionary timescales, the robustness of transcriptional regulation has helped shape life as we know it, by facilitating evolutionary innovations that helped organisms such as flowering plants and vertebrates diversify.


Mechanisms of mutational robustness in transcriptional regulation
Joshua L. Payne and Andreas Wagner

Front. Genet., 27 October 2015 | http://dx.doi.org/10.3389/fgene.2015.00322

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Big data in biomedicine

Big data in biomedicine | Papers | Scoop.it

It may now cost less to sequence the three billion DNA base pairs of a human genome than to do a brain scan. But how does all that genomic data translate into treatment?
Life scientists are bringing together astonishing volumes of information from genomic sequencing, lab studies and patient records. And the resulting era of 'precision medicine' is already delivering treatments tailored to individual needs.
These 'big data' efforts face huge challenges, from creating analytic tools and solving scientific puzzles to accessing millions of gigabytes of data and overcoming barriers to accessing patients' health records



Big data in biomedicine
Eric Bender
Nature 527, S1 (05 November 2015) http://dx.doi.org/10.1038/527S1a ;

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Reputation Effects in Public and Private Interactions

We study the evolution of cooperation based on reputation. This mechanism is called indirect reciprocity. In a world of binary reputations, people help a good individual but do not help a bad one. They also monitor their own reputation to receive reciprocation from others. We propose a novel model of indirect reciprocity where two types of interactions exist. In a public interaction your behavior is always observed by others. In a private interaction, your behavior is less likely to be observed. We study the competition between honest and hypocritical strategies. The former always help good individuals, whereas the latter do so only in private interactions. We describe conditions for the evolution of honest strategies.


Ohtsuki H, Iwasa Y, Nowak MA (2015) Reputation Effects in Public and Private Interactions. PLoS Comput Biol 11(11): e1004527. http://dx.doi.org/10.1371/journal.pcbi.1004527 

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Diversity of immune strategies explained by adaptation to pathogen statistics

Biological organisms have evolved a wide range of immune mechanisms to defend themselves against pathogens. Beyond molecular details, these mechanisms differ in how protection is acquired, processed and passed on to subsequent generations -- differences that may be essential to long-term survival. Here, we introduce a mathematical framework to compare the long-term adaptation of populations as a function of the pathogen dynamics that they experience and of the immune strategy that they adopt. We find that the two key determinants of an optimal immune strategy are the frequency and the characteristic timescale of the pathogens. Depending on these two parameters, our framework identifies distinct modes of immunity, including adaptive, innate, bet-hedging and CRISPR-like immunities, which recapitulate the diversity of natural immune systems.


Diversity of immune strategies explained by adaptation to pathogen statistics
Andreas Mayer, Thierry Mora, Olivier Rivoire, Aleksandra M. Walczak

http://arxiv.org/abs/1511.08836 

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Democracy-Growth Dynamics for Richer and Poorer Countries

We study the empirical relationship between democracy and growth using grid-based panel regression and regime-transition frameworks. Our set-up nests several existing approaches, such as Barro (1996) and Gerring et al. (2005), and reconciles their conflicting messages in a more general model, and we identify the best-fitting discounts and memories. Our main finding is that democracy --best-modelled as a stock variable-- does cause growth, especially beyond the immediate short-run, by enabling the accumulation of physical, human, social and political capitals. Beyond threshold levels of democratic and economic development, however, there are incentives for de-democratization in order to boost short-run growth at the cost of higher sustained long-run growth.


Democracy-Growth Dynamics for Richer and Poorer Countries

Heinrich H. Nax, Anke B Schorr

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2698287

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Quantum Walks with Gremlin

A quantum walk places a traverser into a superposition of both graph location and traversal "spin." The walk is defined by an initial condition, an evolution determined by a unitary coin/shift-operator, and a measurement based on the sampling of the probability distribution generated from the quantum wavefunction. Simple quantum walks are studied analytically, but for large graph structures with complex topologies, numerical solutions are typically required. For the quantum theorist, the Gremlin graph traversal machine and language can be used for the numerical analysis of quantum walks on such structures. Additionally, for the graph theorist, the adoption of quantum walk principles can transform what are currently side-effect laden traversals into pure, stateless functional flows. This is true even when the constraints of quantum mechanics are not fully respected (e.g. reversible and unitary evolution). In sum, Gremlin allows both types of theorist to leverage each other's constructs for the advancement of their respective disciplines.


Quantum Walks with Gremlin
Marko A. Rodriguez, Jennifer H. Watkins

http://arxiv.org/abs/1511.06278

Complexity Digest's insight:

See Also http://tinkerpop.incubator.apache.org 

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Disconnected! The parallel streams of network literature in the natural and social sciences

During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify the study of networks. I conclude by briefly reviewing the historical sources of these differences and by providing an outlook on how these two literatures may come closer together.


Disconnected! The parallel streams of network literature in the natural and social sciences
Cesar A. Hidalgo

http://arxiv.org/abs/1511.03981

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Transport: A roadblock to climate change mitigation?

If current trends continue, the global number of light-duty vehicles will roughly double by midcentury, driven by rising affluence especially in China, India, and South East Asia (3). Demand for freight transport (road, rail, shipping, and air) and passenger aviation is projected to surge as well. In recent years, CO2 emissions from transport have stabilized in the European Union and the United States as fuel economy and emission standards were tightened. Municipalities worldwide have implemented local measures to reduce emissions of urban transport systems. However, these efforts have not been able to slow sectoral emission growth on the global level; there needs to be a broader suite of complementary, and enforced, policies in order to succeed.


Transport: A roadblock to climate change mitigation?
Felix Creutzig, Patrick Jochem, Oreane Y. Edelenbosch, Linus Mattauch, Detlef P. van Vuuren, David McCollum, Jan Minx

Science 20 November 2015:
Vol. 350 no. 6263 pp. 911-912
http://dx.doi.org/10.1126/science.aac8033

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Cognitive Processing, Volume 16, Issue 4, Special Section: Complexity in brain and cognition

Cognitive Processing, Volume 16, Issue 4, Special Section: Complexity in brain and cognition | Papers | Scoop.it

This special issue contains a collection of papers from the 2013 conference of the Society for Complex Systems in Cognitive Science (SCSCS), held as a satellite of the Cognitive Science conference in Berlin in July of that year. The SCSCS is aiming to promote the use of complex systems theory (CST) in cognitive science. Occasionally, when cognitive scientists encounter complex systems the- ory, it is not at all clear that CST is actually relevant to cognitive science.

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Self-propelled Chimeras

We report the appearance of chimera states in a minimal extension of the classical Vicsek model for collective motion of self-propelled particle systems. Inspired by earlier works on chimera states in the Kuramoto model, we introduce a phase lag parameter in the particle alignment dynamics. Compared to the oscillatory networks with fixed site positions, the self-propelled particle systems can give rise to distinct forms of chimeras resembling moving flocks through an incoherent surrounding, for which we characterize their parameter domains. More specifically, we detect localized directional one-headed and multi-headed chimera states, as well as scattered directional chimeras without space localization. We discuss canonical generalizations of the elementary Vicsek model and show chimera states for them indicating the universality of this novel behavior. A continuum limit of the particle system is derived that preserves the chimeric behavior.


Self-propelled Chimeras
Nikita Kruk, Yuri Maistrenko, Nicolas Wenzel, Heinz Koeppl

http://arxiv.org/abs/1511.04738

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Bird song: a model complex adaptive system

Bird songs make an attractive model for studying complex systems. They may range from simple repeated sequences, to complex sequences of different phrase types, much like human language. There is probably no single way to best characterize their complexity. We should avoid saying that that “bird songs are in complexity class X”. The diversity of examples suggests that the song of one bird species or another can probably be found to exemplify and model many kinds of complex systems. We suggest that the complexity classes for cellular automata distinguished by Wolfram might give some insight into the capacity of bird songs to transmit information and of the complexity needed to generate them.

Bird song: a model complex adaptive system
Charles E. Taylor , Martin L. Cody
Artificial Life and Robotics
http://link.springer.com/article/10.1007/s10015-015-0231-z

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Oceans of change

The phrase “climate change” typically evokes thoughts of rising air temperatures or other atmospheric phenomena such as droughts and extreme storms. Much less often do we consider the parallel changes that are occurring in the oceans, despite their extent and importance.
Climate change in the oceans has many facets. One is a rise in sea levels. Scientists are learning about how previous warm periods altered sea levels, and what that past may tell us about the future. To help us cope, so-called green infrastructure, such as planted marshes or oyster reefs, may help protect low-lying shorelines. Climate change is also creating problems for fisheries; for example, commercially valuable stocks move in response to warming seas.


Oceans of change
Julia Fahrenkamp-Uppenbrink, David Malakoff, Jesse Smith, Caroline Ash, Sacha Vignieri

Science 13 November 2015:
Vol. 350 no. 6262 pp. 750-751
http://dx.doi.org/10.1126/science.350.6262.750

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Higher costs of climate change

An attempt to reconcile the effects of temperature on economic productivity at the micro and macro levels produces predictions of global economic losses due to climate change that are much higher than previous estimates. 


Higher costs of climate change
Thomas Sterner
Nature 527, 177–178 (12 November 2015) http://dx.doi.org/10.1038/nature15643

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A rhythm landscape approach to the developmental dynamics of birdsong

Unlike simple biological rhythms, the rhythm of the oscine bird song is a learned time series of diverse sounds that change dynamically during vocal ontogeny. How to quantify rhythm development is one of the most important challenges in behavioural biology. Here, we propose a simple method, called ‘rhythm landscape’, to visualize and quantify how rhythm structure, which is measured as durational patterns of sounds and silences, emerges and changes over development. Applying this method to the development of Bengalese finch songs, we show that the rhythm structure begins with a broadband rhythm that develops into diverse rhythms largely through branching from precursors. Furthermore, an information-theoretic measure, the Jensen–Shannon divergence, was used to characterize the crystallization process of birdsong rhythm, which started with a high rate of rhythm change and progressed to a stage of slow refinement. This simple method provides a useful description of rhythm development, thereby helping to reveal key temporal constraints on complex biological rhythms.


Sasahara K, Tchernichovski O, Takahasi M, Suzuki K, Okanoya K. Journal of the Royal Society Interface 12: 20150802. http://dx.doi.org/10.1098/rsif.2015.0802

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